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The interaction between the surgical instrument and tissue is a yet little-exploited source of information. Vibroacoustic signals resulting from an interaction can be analyzed to extract relevant information related to the tissue. This work presents a summary of feasibility studies with synthetic-, animal-, and ex-vivo tissue specimen. A standard palpation probe is equipped with a vibration measurement system. A manual and robot-assisted setup is used for data acquisition. Simple features derived from the signals are used in a subsequent classification step using kNN and SVM. The obtained results show the capability to differentiate various tissues, such as subchondral bone with an accuracy of 98.44%. This demonstrates the potential of the approach to provide tissue related information based on instrument interactions.

This work presents a novel approach for detecting underlying pulsatile structures with laparoscopic tools by using proximal vibroacoustic sensing. This new method could detect pulsations at a depth of up to 1.5 cm. The outcome of this work indicates the potential of identifying deep-seated pulsatile structures during robot-assisted laparoscopy, where haptic feedback is highly limited, to help surgeons prevent vascular damage that may lead to injuries.

Minimally Invasive Robotic Surgery (MIRS) has emerged as a transformative approach in surgical practice, offering reduced patient trauma and enhanced precision. However, challenges persist, including the loss of tactile feedback for surgeons. This study explores the application of machine learning algorithms, specifically variational autoencoders, in vibro-acoustic (VA) signal analysis to address this issue. Our comparative analysis evaluates the potential of supervised learning in surgical data analysis, contributing to advancements in surgical technology. Despite achieving an accuracy of 81%, our results indicate opportunities for further refinement, considering the superior accuracies reported in previous studies. This research underscores the importance of innovative approaches in medical data analysis for optimizing patient care in minimally invasive surgery.

This paper presents the use of vibro-acoustic sensing to augment laparoscopic surgery procedures by analyzing the signals produced during cutting and palpation tasks on various tissue samples. Vibro-acoustic signals were acquired during an experiment on a dedicated phantom covered in dense foam, where three trocars were inserted into the phantom to place the endoscopic camera and two laparoscopic instruments. The results of the signals analysis demonstrate the potential of this approach for making laparoscopic interactions audible, differentiating between tissue types, and detecting variations in tissue properties. Vibro-acoustic sensing could be a valuable tool for integrating sound into the current clinical workflow for enhancing endoscope video images.

The overall complication rate during laparoscopic access is estimated to be as high as 14 %. Surgeons have to rely heavily on their experience and haptic perception while inserting the Veress needle or a trocar into the peritoneal cavity. Surgical Audio Guidance (SURAG) is a promising alternative to current techniques. It acquires instrument-born vibroacoustic (VA) waves to track the insertion of the instrument and provide real-time feedback to surgeons. This article presents an initial evaluation of the SURAG technology through two sets of experiments to classify Veress needle events using different AI-models. The results demonstrate the feasibility of using AI for classifying Veress needle events and the potential of the SURAG technology to support surgeons during laparoscopic access and minimally invasive needle interventions in general.

Minimal-invasive procedures come with significant advantages for the patient. They also come with problems as the navigation/guidance of the devices to a target location is either based on pre-operatively acquired images and then performed free-hand or is accompanied by intraoperative imaging such as MRI or CT that is expensive, complicated and produces artifacts. Using robotic systems for moving and guiding these interventional and therapeutic devices adds additional issues like lack of palpation sensation and missing tissue feedback. While it is possible to add sensors to the distal tip, this creates other obstacles concerning reduced functionality, cables, sterility issues and added complexity and cost. We propose to use a proximally attached audio sensor to record the tissue tool interaction and provide real-time feedback to the clinician. This paper reports on initial attempts to use this technology with robotic arms for surface characterization and interventional vascular procedures that gain increased attention in combination with robotic devices. In summary, Proximal Audio Sensing could be a versatile, cost-effective and powerful tool to guide minimally invasive needle interventions and enable (semi-) autonomous robot-assisted surgery.

About 50 % of complications during laparoscopy occur when surgical access is created. The Veress needle and proposed technical alternatives do not provide reliable information to support the surgeons in guiding the needle, or the feedback is not clearly perceivable. Based on acoustic emissions, Surgical Audio Guidance (SURAG) proposes a non-invasive and efficient way to enhance the perception of guidance information through acoustic and visual feedback displayed in real-time. This article demonstrates that the developed feedback matches the information about tissue layer crossings provided by force measurements. This indicates that SURAG can provide an effective means to make laparoscopic access more precise and safe, especially in pediatric surgery, where space for placing the needle is minimal.

Under-staffing of nurses is a significant problem in most countries. It is expected to rise in the coming years, making it challenging to perform crucial tasks like assessing a patient's condition, assisting the surgeon in medical procedures, catheterization and Blood Transfusion etc., Automation of some essential tasks would be a viable idea to overcome this shortage of nurses. One such task intended to automate is the role of a 'Scrub Nurse' by using a robotic arm to hand over the surgical instruments. In this project, we propose to use a Collaborative Robotic-arm as a Scrub nurse that can be controlled with voice commands. The robotic arm was programmed to reach the specified position of the instruments placed on the table equipped with a voice recognition module to recognize the requested surgical instrument. When the Surgeon says "Pick Instrument", the arm picks up the instrument from the table and moves it over to the prior defined handover position. The Surgeon can take over the instrument by saying the command "Drop". Safe pathways for automatic movement of arm and handover position will be predefined by the Surgeon manually. This concept was developed considering the convenience of the Surgeon and the patient's safety, tested for collision, noisy environments, positioning failures and accuracy in grasping the instruments. Limitations that need to be considered in future work are the recognition of voice commands which as well as the returning of the instruments by the surgeon in a practical and safe way.

This work aims to demonstrate the feasibility that haptic information can be acquired from a da Vinci robotic tool using audio sensing according to sensor placement requirements in a real clinical scenario. For that, two potential audio sensor locations were studied using an experimental setup for performing, in a repeatable way, interactions of a da Vinci forceps with three different tissues. The obtained audio signals were assessed in terms of their resulting signal-to-noise-ratio (SNR) and their capability to distinguish between different tissues. A spectral energy distribution analysis using Discrete Wavelet Transformation was performed to extract signal signatures from the tested tissues. Results show that a high SNR was obtained in most of the audio recordings acquired from both studied positions. Additionally, evident spectral energy-related patterns could be extracted from the audio signals allowing us to distinguish between different palpated tissues.

Arthroscopic surgery is a technically challenging but common minimally invasive procedure with a long learning curve and a high incidence of iatrogenic damage. These damages can occur due to the lack of feedback and supplementary information regarding tissue-instrumentcontact during surgery. Deliberately performed interactions can be used however to obtain clinically relevant information, e.g. when a surgeon uses the tactile feedback to assess the condition of articular cartilage. Yet, the perception of such events is highly subjective. We propose a novel proximally attached sensing concept applied to arthroscopic surgery to allow an objective characterization and utilization of interactions. It is based on acoustic emissions which originate from tissue-instrument-contact, that propagate naturally via the instrument shaft and that can be obtained by a transducer setup outside of the body. The setup was tested on its ability to differentiate various conditions of articular cartilage. A femoral head with varying grades of osteoarthritic cartilage was tapped multiple times ex-vivo with a conventional Veress needle with a sound transducer attached at the outpatient end. A wavelet-based processing of the obtained signals and subsequent analysis of distribution of spectral energy showed the potential of tool-tissue-interactions to characterize different cartilage conditions. The proposed concept needs further evaluation with a dedicated design of the palpation tool and should be tested in realistic arthroscopic scenarios.

Cerebrovascular diseases such as stenosis, atherosclerosis or distention of the carotid artery are accountable for about 1 million death per year across Europe. Diagnostic tools like ultrasound imaging, angiography or magnetic resonance-based imaging require specific hardware and highly depend on the experience of the examining clinician. In contrast auscultation with a stethoscope can be used to screen for carotid bruits - audible vascular sounds associated with turbulent blood flow - a method called phonoangiography. A reliable auscultation setup is prerequisite to ensure high signal quality, adequate processing and the objective evaluation of this audible signal. We propose a computer assisted auscultation system for the acquisition of vascular sounds of the carotid. The system comprises of an auscultation device, a smartphone-based control application and cloud-based signal analysis and storage. It is designed to facilitate the objective assessment, screening and monitoring of long-term changes in the vessel condition based on auscultation of the carotid artery.

The location of a puncture needle’s tip and the resistance of tissue against puncture are crucial information for clinicians during a percutaneous procedure. The tip location and needle alignment can be observed by image guidance. Tactile information caused by tissue resistance to rupture, allow clinicians the perception of structural changes during puncture. Nevertheless, this sense is individual and subjective. To improve percutaneous procedures, the implementation of transducers to enhance or complement the senses offer objective feedback to the user. Known approaches are e.g. based on integrated force sensors. However, this is connected to higher device costs, sterilization and certification issues. A recent publication shows the implementation of an audio transducer clipped at the proximal end of the needle. This sensor is capable of acquiring emitted sounds of the distal tiptissue interaction that are transmitted over the needle structure. The interpretation of the measured audio signals is highly depended on the transmission over the needle, the tissue and, the penetration depth. To evaluate the influence of these parameters, this work implements a simplified experimental setup in a controlled environment with a minimum of noise and without micro tremors induced by clinician’s hands. A steel rod simulating a needle is inserted into pork meat of different thickness. A controlled impact covering the needle’s tip mimics tissue contact. The resulting signals are recorded and analyzed for better understanding of the system.

Robotic surgeries are still limited with respect to the surgeon’s natural senses. The tactile sense is exceptional important in conventional clinical procedures. To identify critical structures inside the tissue, palpation is a commonly used technique in conventional open surgeries. The underlying organ or pathological structures conditions (healthy, abnormally hard or soft) can for example be localized and assessed through this process. Palpation needs a tactile sense; however, that is commonly not available or limited in robotic surgeries. The palpation need was already addressed by several research groups that integrated complex sensor-feedback-systems into prototype surgical instruments for robotic systems. We propose a new technique to acquire data of the tissue tool interaction of the surgical instruments. The structure borne transmission path is used to measure acoustic emission (AE) at the outpatient (proximal) end of the instruments with the help of different sensors attached to the surface of the surgical tool. Initial tests were performed using a microphone in combination with a stethoscope. This setup showed promising results and a more integrated prototype was subsequently designed. A piezoelectric charge accelerometer was used as vibration sensor and compared to a MEMS microphone. A signal acquisition system was developed to acquire signals from both sensors in parallel. The sensors were then attached onto the shaft of a daVinci Prograsp Forceps instrument. According to the surgery observation, a series of simulated experiments was conducted. The tip of the grasper was swiped manually over a human subject’s dorsal and palmar hand side, lateral side of neck and over the carotid artery. Additionally, contact with soft tissue and other instruments were evaluated since these are events of interest during surgery. Advanced signal processing techniques allowed the identification and characterization of significant events such as palpation dynamics, contact and pulsation. Signals acquired by the MEMS microphone showed the most promising results. This approach will now be used to build a prototype for further evaluation in a clinical setup. The paper presents the first results that show that this novel technique can provide valuable information about the tool-tissue interaction in robotic surgery that typically can only be obtained through advanced distal sensor systems or actual human touch.

Laparoscopic access, a critical yet challenging step in surgical procedures, often leads to complications. Existing systems, such as improved Veress needles and optical trocars, offer limited safety benefits but come with elevated costs. In this study, a prototype of a novel technology for guiding needle interventions based on vibroacoustic signals is evaluated in porcine cadavers. The prototype consistently detected successful abdominal cavity entry in 100% of cases during 193 insertions across eight porcine cadavers. The high signal quality allowed for the precise identification of all Veress needle insertion phases, including peritoneum puncture. The findings suggest that this vibroacoustic-based guidance technology could enhance surgeons' situational awareness and provide valuable support during laparoscopic access. Unlike existing solutions, this technology does not require sensing elements in the instrument's tip and remains compatible with medical instruments from various manufacturers. [ABSTRACT FROM AUTHOR]

Introduction: Atherosclerotic diseases of the carotid are a primary cause of cerebrovascular events such as stroke. For the diagnosis and monitoring angiography, ultrasound- or magnetic resonance-based imaging is used which requires costly hardware. In contrast, the auscultation of carotid sounds and screening for bruits - audible patterns related to turbulent blood flow - is a simple examination with comparably little technical demands. It can indicate atherosclerotic diseases and justify further diagnostics but is currently subjective and examiner dependent., Methods: We propose an easy-to-use computer-assisted auscultation system for a stable and reproducible acquisition of vascular sounds of the carotid. A dedicated skin-transducer-interface was incorporated into a handheld device. The interface comprises two bell-shaped structures, one with additional acoustic membrane, to ensure defined skin contact and a stable propagation path of the sound. The device is connected wirelessly to a desktop application allowing real-time visualization, assessment of signal quality and input of supplementary information along with storage of recordings in a database. An experimental study with 5 healthy subjects was conducted to evaluate usability and stability of the device. Five recordings per carotid served as data basis for a wavelet-based analysis of the stability of spectral characteristics of the recordings., Results: The energy distribution of the wavelet-based stationary spectra proved stable for measurements of a particular carotid with the majority of the energy located between 3 and 40 Hz. Different spectral properties of the carotids of one individual indicate the presence of sound characteristics linked to the particular vessel. User-dependent parameters such as variations of the applied contact pressure appeared to have minor influence on the general stability., Conclusion: The system provides a platform for reproducible carotid auscultation and the creation of a database of pathological vascular sounds, which is a prerequisite to investigate sound-based vascular monitoring., Competing Interests: TS, AB, AI and MF report a patent “MEDICAL ACOUSTIC SENSOR SYSTEM” (US 62/756487) pending. Stefan Hellwig reports a patent, Bloxton US-Patent pending. Rutuja Salvi and Stefan Hellwig are employees of IDTM GmbH. The authors report no other potential conflicts of interest in this work., (© 2020 Sühn et al.)